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K2view has been recognized by CIOCoverage magazine as 1 of the top 10 data integration companies to watch in 2023. Read on to learn why.
Table of Contents
Alignment with 2023 Data Integration Trends
A Data Integration Platform and More
Innovative Data Integration Technology
Data Product Platform Use Case
Plans for the Future
Alignment with 2023 Data Integration Trends
In its coverage of K2view as a top-10 data integration company, CIOCoverage magazine interviews CTO, Yuval Perlov, who describes how B2C enterprise data is scattered across multiple systems, from on-prem to the cloud. To leverage that wealth of information, Perlov explains how the data must first be identified, aggregated, integrated, and frequently anonymized to make it compliant with data privacy regulations. Therefore, for most enterprises, data initiatives are always complex mega-projects.
To simplify data management, Perlov describes 2 major data integration trends are gaining ground:
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Optimizing speed and cost savings, through citizen data engineering, data migration to the cloud, legacy application modernization, and self-service data provisioning.
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Treating data as a product, with emerging data fabric architecture and data mesh architecture.
A Data Integration Platform and More
These trends are elegantly addressed using the K2view Data Product Platform, which builds and manages a complete and compliant dataset for every business entity in the form of a data product – in real time. The dataset is constantly in sync with its underlying source systems, adapts to any changes in the source structures, and is instantly accessible to authorized data consumers. The data product consists of the business entity data, and everything else that’s required to deliver it.
The K2view platform, which deploys in cloud, on-prem, or hybrid environments, provides data teams with a low-code/no-code framework for managing the entire data product lifecycle – design, engineering, testing, deployment, and ongoing change management. It fuels real-time operational use cases, including test data management, data masking, synthetic data generation, data tokenization, customer 360, and more – to deliver business outcomes in significantly less time, and at lower cost, than other data integration tools.
Innovative Data Integration Technology
K2view Data Product Platform concurrently manages thousands of data products, that continually sync, transform, mask, and provision trusted data via millions of Micro-Databases™ – one for each business entity instance. This patented technology lets data teams define the data product (entity and transformations) once, for use in many subsequent workloads, at scale.
In summary, a Micro-Database is:
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Accessible in real time
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In sync with all underlying sources
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Protected with its own 256-bit encryption key
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Compliant with data privacy regulations
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Cached, persisted, or virtualized, for optimum performance
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Trusted as the golden record for the business entity
Data Product Platform Use Case
A Fortune 20 telco implemented Data Product Platform in 2015. Today it supports multiple use cases, including:
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x360 – Providing a complete view of any business entity (order, location, or device), with the platform managing hundreds of millions of such entities at the same time.
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Legacy application modernization – Migrating legacy systems to the cloud, and allowing for real-time data delivery using secure data services.
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Data privacy compliance – Masking, delivering, and purging customer data to assure regulatory compliance, while minimizing the cost of data processes and compliance workflows.
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Test data management – Offering data teams a self-service portal to instantly provision realistic test data subsets, significantly reducing test data provisioning time.
Plans for the Future
From a market perspective, K2view intends to expand its current focus on large, B2C enterprises to include mid-size companies in its key target markets.
From a product perspective, K2view is accelerating the pace of embedding AI into its platform, to provide more automation and recommendations, increase data engineering productivity, and deliver better, smarter data products.
